Difference between revisions of "Domain Independent Model for Product Attribute Extraction from User Reviews Using Wikipedia"
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| − | '''Domain Independent Model for Product Attribute Extraction from User Reviews Using Wikipedia''' - scientific work related to Wikipedia quality published in 2011, written by Sudheer Kovelamudi, Sethu Ramalingam, Arpit Sood and Vasudeva Varma. | + | '''Domain Independent Model for Product Attribute Extraction from User Reviews Using Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2011, written by [[Sudheer Kovelamudi]], [[Sethu Ramalingam]], [[Arpit Sood]] and [[Vasudeva Varma]]. |
== Overview == | == Overview == | ||
| − | The world of E-commerce is expanding, posing a large arena of products, their descriptions, customer and professional reviews that are pertinent to them. Most of the product attribute extraction techniques in literature work on structured descriptions using several text analysis tools. However, attributes in these descriptions are limited compared to those in customer reviews of a product, where users discuss deeper and more specific attributes. In this paper, authors propose a novel supervised domain independent model for product attribute extraction from user reviews. The user generated content contains unstructured and semi-structured text where conventional language grammar dependent tools like parts-of-speech taggers, named entity recognizers, parsers do not perform at expected levels. Authors used Wikipedia and Web to identify product attributes from customer reviews and achieved F1score of 0.73. | + | The world of E-commerce is expanding, posing a large arena of products, their descriptions, customer and professional reviews that are pertinent to them. Most of the product attribute extraction techniques in literature work on structured descriptions using several text analysis tools. However, attributes in these descriptions are limited compared to those in customer reviews of a product, where users discuss deeper and more specific attributes. In this paper, authors propose a novel supervised domain independent model for product attribute extraction from user reviews. The user generated content contains unstructured and semi-structured text where conventional language grammar dependent tools like parts-of-speech taggers, [[named entity]] recognizers, parsers do not perform at expected levels. Authors used [[Wikipedia]] and Web to identify product attributes from customer reviews and achieved F1score of 0.73. |
Revision as of 12:30, 3 February 2020
Domain Independent Model for Product Attribute Extraction from User Reviews Using Wikipedia - scientific work related to Wikipedia quality published in 2011, written by Sudheer Kovelamudi, Sethu Ramalingam, Arpit Sood and Vasudeva Varma.
Overview
The world of E-commerce is expanding, posing a large arena of products, their descriptions, customer and professional reviews that are pertinent to them. Most of the product attribute extraction techniques in literature work on structured descriptions using several text analysis tools. However, attributes in these descriptions are limited compared to those in customer reviews of a product, where users discuss deeper and more specific attributes. In this paper, authors propose a novel supervised domain independent model for product attribute extraction from user reviews. The user generated content contains unstructured and semi-structured text where conventional language grammar dependent tools like parts-of-speech taggers, named entity recognizers, parsers do not perform at expected levels. Authors used Wikipedia and Web to identify product attributes from customer reviews and achieved F1score of 0.73.
